Quantitative and qualitative results are shown in Table  and Fig. REF  respectively. Comparison points include state-of-art 6D pose tracking methods that use object CAD models, such as RGF [1]}, dbot PF [2]} and \(se(3)\) -TrackNet [3]}. 6-PACK [4]} is a state-of-art 6D pose tracking approach relying on category-level 3D models. Its evaluation on objects “021_bleach_cleanser”, “006_mustard_bottle” and “005_tomato_soup_can” are performed by using the officially releasedhttps://github.com/j96w/6-PACK networks trained on “bottle” and “can” category respectively . For the rest of the objects “003_cracker_box” and “004_sugar_box”, no suitable corresponding category can be found in existing 3D model database [5]} and thus 6-PACK is not able to be retrained and evaluated on them. For 6-PACK, 3D bounding box of the object model, computed from forward kinematics, is provided in every frame to crop ROI from point cloud, since it is more reliable than its default module of extrapolating the 3D bounding box by estimated motion.
For MaskFusion [6]} and BundleTrack, the initial object mask is obtained by table fitting and removal, followed by Euclidean Clustering implemented in PCL [7]}. The original MaskFusion's segmentation module Mask-RCNN cannot be retrained on this benchmark due to the lack of training set. Therefore, during tracking, the target object mask is computed by segmenting out the region of robot arm and end-effector from forward kinematics. For instances of irregular shapes or colors (“021_bleach_cleanser”, “006_mustard_bottle”) within the “bottle” category that 6-PACK has been trained on, it struggles to get satisfactory result. Nevertheless, BundleTrack consistently demonstrates high quality tracking without any retraining or fine-tuning. This establishes generalizability of BundleTrack to novel object instances regardless of their out-of-distribution properties within the category. BundleTrack also achieves comparable or superior performance even when compared against methods relying on object instance CAD models [1]}, [2]}, [3]}.
